Image evaluation method, image evaluation device, and storage medium having program stored thereon

ABSTRACT

In the present invention, an image evaluation method includes: (a) a step of acquiring image information to be evaluated; (b) a step of setting an evaluation area for the acquired image information, the evaluation area being smaller than an area corresponding to the image information; (c) a step of obtaining an individual evaluation on the acquired image information; (d) a step of obtaining another individual evaluation on the acquired image information; and (e) a step of obtaining a comprehensive evaluation based on the individual evaluation and the other individual evaluation. Herein, the individual evaluation is obtained by individually evaluating a predetermined item in the set evaluation area. The other individual evaluation is obtained by individually evaluating another predetermined item.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority on Japanese Patent Application No.2004-332340 filed Nov. 16, 2004 and Japanese Patent Application No.2005-57450 filed Mar. 2, 2005, which are herein incorporated byreference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to image evaluation methods, imageevaluation devices, and storage media having programs stored thereon.

2. Description of the Related Art

Various kinds of methods have been proposed for evaluating imageinformation that is generated by a photographic device or the like. Forexample, there have been proposed a method for evaluating motion blurs(for example, see “Identification of Blur Parameters from Motion BlurredImage,” Y. Yizhaky and N. S. Kopeika, GRAPHICAL MODELS AND IMAGEPROCESSING Vol. 59, No. 5, September, pp. 310-320, 1997) and a methodfor evaluating noise in images occurring from JPEG (Joint PhotographicExperts Group) compression (for example, see “A Generalized Block-EdgeImpairment Metric for Video Coding,” H. R. Wu and M. Yuen, IEEE SIGNALPROCESSING LETTERS, VOL. 4, NO. 11, NOVEMBER 1997).

These methods have had the problem, however, that the evaluation itemsare evaluated individually, and it is thus difficult to make anappropriate evaluation from the resulting items alone.

SUMMARY OF THE INVENTION

It is thus an object of the present invention to evaluate imageinformation more appropriately and make the evaluation efficiently aswell.

To achieve the foregoing object, a main invention provides the followingimage evaluation method.

That is, an image evaluation method includes:

(a) a step of acquiring image information to be evaluated;

(b) a step of setting an evaluation area for the acquired imageinformation, the evaluation area being smaller than an areacorresponding to the image information;

(c) a step of obtaining an individual evaluation on the acquired imageinformation, the individual evaluation being obtained by individuallyevaluating a predetermined item in the set evaluation area;

(d) a step of obtaining an other individual evaluation on the acquiredimage information, the other individual evaluation being obtained byindividually evaluating an other predetermined item; and

(e) a step of obtaining a comprehensive evaluation based on theindividual evaluation and the other individual evaluation.

The features and objects of the present invention other than thosedescribed above will become more apparent from the description of thisspecification when read with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram for explaining the appearance of a printer and aCD-R drive connected to this printer;

FIG. 2 is a block diagram for explaining the electric configuration ofthe printer;

FIG. 3 is a flowchart for explaining the main processing of the printer;

FIG. 4 is a flowchart for explaining evaluation and backup processing(S31) in the main processing;

FIG. 5 is a diagram for explaining image evaluation processing (S120) inthe backup process;

FIG. 6A is a conceptual diagram for explaining image information whichis divided into a predetermined number of blocks;

FIG. 6B is a conceptual diagram for explaining differences in luminancebetween adjoining pixels;

FIG. 6C is a conceptual diagram showing an evaluation area whichincludes a block having a maximum number of edges;

FIG. 7A is a diagram for explaining a Sobel filter in an X direction foruse when generating an edge image;

FIG. 7B is a diagram for explaining a Sobel filter in a Y direction foruse when generating an edge image;

FIG. 7C is a diagram for explaining an area of 3×3 pixels with a givenpixel P(i, j) at the center, and the luminances Y of the pixels P inthis area;

FIG. 8A is a diagram schematically explaining the application of theSobel filters;

FIG. 8B is a diagram schematically explaining a gradient magnitude a;

FIG. 9A is a schematic diagram for explaining an image having a sharpedge;

FIG. 9B is a graph of the luminance corresponding to the edge of FIG.9A;

FIG. 9C is a schematic diagram for explaining an image having a blurrededge;

FIG. 9D is a graph of the luminance corresponding to the edge of FIG.9C;

FIG. 10 is a diagram schematically showing classified blocks (BK2);

FIG. 11 is a schematic diagram showing stepwise how the blocks arereclassified by object division processing;

FIG. 12 is a schematic diagram for explaining the relationship betweenthe direction of edge intensity (θmax) and the direction of a motionblur (θmax+π/2);

FIG. 13 is a diagram for explaining pixels adjoining in one direction,and a function f(n) obtained from the luminances corresponding to therespective pixels;

FIG. 14 is a schematic diagram for explaining an autocorrelationfunction;

FIG. 15 is an explanatory diagram showing an example of luminance,differences in the intensity of luminance, and the smoothness of changesin luminance;

FIG. 16 is a conceptual diagram showing the correlation between adetermination factor and a comprehensive evaluation value, showing avariation by weighting;

FIG. 17 is a conceptual diagram showing the comprehensive evaluationvalue when determined based on a defocus evaluation value and a motionblur evaluation value;

FIG. 18 is a flowchart for explaining processing for confirming imageinformation to be backed up; and

FIG. 19 is a diagram for explaining an example of display of the imageinformation backed up.

For a more complete understanding of the present invention andadvantages thereof, reference is had to the following description, takenin conjunction with the accompanying drawings.

DETAILED DESCRIPTION OF THE INVENTION

From the description of this specification and that of the accompanyingdrawings, at least the following will become apparent.

Initially, it will become clear that an image evaluation method descriedbelow can be achieved.

That is, an image evaluation method includes: (a) a step of acquiringimage information to be evaluated; (b) a step of setting an evaluationarea for the acquired image information, the evaluation area beingsmaller than an area corresponding to the image information; (c) a stepof obtaining an individual evaluation on the acquired image information,the individual evaluation being obtained by individually evaluating apredetermined item in the set evaluation area; (d) a step of obtainingan other individual evaluation on the acquired image information, theother individual evaluation being obtained by individually evaluating another predetermined item; and (e) a step of obtaining a comprehensiveevaluation based on the individual evaluation and the other individualevaluation.

According to such an image evaluation method, the comprehensiveevaluation that is obtained provides a more appropriate evaluation sincethe individual evaluation and the other individual evaluation arereflected thereon. Moreover, since the individual evaluation is madebased on an evaluation in the evaluation area, the processing can beperformed efficiently.

In the foregoing image evaluation method, it is preferable that in thestep of setting the evaluation area, the acquired image information isdivided into a plurality of blocks, and the evaluation area is set basedon a block having a maximum number of edges.

According to such an image evaluation method, it is possible to improvethe accuracy of the individual evaluation.

In the foregoing image evaluation method, it is preferable that in thestep of setting the evaluation area, a luminance of each pixel isacquired from the acquired image information, and the evaluation area isset so that a section in which a difference in the luminance of adjacentpixels is largest is located at the center.

According to such an image evaluation method, it is possible to improvethe accuracy of the individual evaluation.

In the foregoing image evaluation method, it is preferable that thepredetermined item is one selected from between an item indicating adegree of defocus and an item indicating a degree of a motion blur.

According to such an image evaluation method, it is possible to furtherimprove the accuracy of the comprehensive evaluation.

In the foregoing image evaluation method, it is preferable that theother predetermined item includes at least one item out of the itemindicating the degree of defocus, the item indicating the degree of amotion blur, and an item indicating a degree of noise occurring atcompression time, the at least one item being an item other than the oneselected as the predetermined item.

According to such an image evaluation method, it is possible to furtherimprove the accuracy of the comprehensive evaluation.

In the foregoing image evaluation method, it is preferable that theother predetermined item includes two items out of the item indicatingthe degree of defocus, the item indicating the degree of a motion blur,and the item indicating the degree of noise occurring at compressiontime, the two items being items other than the one selected as thepredetermined item.

According to such an image evaluation method, it is possible to furtherimprove the accuracy of the comprehensive evaluation.

In the foregoing image evaluation method, it is preferable that theother individual evaluation is obtained by individually evaluating theother predetermined item in the set evaluation area.

According to such an image evaluation method, the processing can beperformed efficiently since the other individual evaluation is obtainedbased on an evaluation in the evaluation area.

In the foregoing image evaluation method, it is preferable that in thestep of setting the evaluation area, the acquired image information isdivided into a plurality of blocks, and the evaluation area is set basedon a block having a maximum number of edges.

According to such an image evaluation method, it is possible to improvethe accuracy of the other individual evaluation.

In the foregoing image evaluation method, it is preferable that in thestep of setting the evaluation area, a luminance of each pixel isacquired from the acquired image information, and the evaluation area isset so that a section in which a difference in the luminance of adjacentpixels is largest is located at the center.

According to such an image evaluation method, it is possible to improvethe accuracy of the other individual evaluation.

In the foregoing image evaluation method, it is preferable that in thestep of obtaining the comprehensive evaluation, the comprehensiveevaluation is obtained based on the individual evaluation and the otherindividual evaluation given weights, respectively.

According to such an image evaluation method, the comprehensiveevaluation that is obtained will provide a more appropriate evaluation.

In the foregoing image evaluation method, it is preferable that in thestep of obtaining the comprehensive evaluation, the comprehensiveevaluation is obtained based on the individual evaluation and the otherindividual evaluation given weights, respectively, the weightsincreasing with increasing impact on image quality.

According to such an image evaluation method, the comprehensiveevaluation that is obtained will provide a more appropriate evaluation.

In the foregoing image evaluation method, it is preferable that in thestep of obtaining the comprehensive evaluation, the comprehensiveevaluation is obtained by using a Gaussian function.

According to such an image evaluation method, it is possible to furtherimprove the accuracy of the comprehensive evaluation.

In the foregoing image evaluation method, it is preferable that themethod further includes an evaluation attachment step of attaching theindividual evaluation, the other individual evaluation, and thecomprehensive evaluation to the acquired image information.

According to such an image evaluation method, it is possible to reflectthe evaluation contents in subsequent processing, for example, in theprocessing by a computer program such as a retouch software applicationor by a printing apparatus such as a printer.

In the foregoing image evaluation method, it is preferable that themethod further includes a step of obtaining an individual evaluation notto be included in the comprehensive evaluation by individuallyevaluating the acquired image information for a nontarget item that isnot to be included in the comprehensive evaluation.

According to such an image evaluation method, it is possible to providemore detailed evaluations.

In the foregoing image evaluation method, it is preferable that in thestep of obtaining the individual evaluation not to be included in thecomprehensive evaluation, an item indicating a degree of focus on anarea different from a subject intended for shooting is evaluated as thenontarget item.

According to such an image evaluation method, it is possible to providemore detailed evaluations.

In the foregoing image evaluation method, it is preferable that themethod further includes another evaluation attachment step of attachingthe individual evaluation not to be included in the comprehensiveevaluation to the acquired image information.

According to such an image evaluation method, it is possible to reflectthe evaluation contents in subsequent processing, for example, inprocessing by a computer program such as a retouch software applicationor by a printing apparatus such as a printer.

An image evaluation device described below can also be achieved.

That is, an image evaluation device includes: a memory for storing imageinformation to be evaluated; and a controller that performs: a step ofacquiring the image information to be evaluated; a step of setting anevaluation area for the acquired image information; a step of obtainingan individual evaluation on the acquired image information, theindividual evaluation being obtained by individually evaluating apredetermined item in the set evaluation area; a step of obtaining another individual evaluation on the acquired image information, the otherindividual evaluation being obtained by individually evaluating an otherpredetermined item; and a step of obtaining a comprehensive evaluationbased on the individual evaluation and the other individual evaluation.

In the foregoing image evaluation device, it is preferable that: thememory stores image information to be printed as the image informationto be evaluated; and the controller further performs a step of attachingthe individual evaluation, the other individual evaluation, and thecomprehensive evaluation to the image information to be evaluated togenerate image information intended for printing.

A storage medium having a program described below can also be achieved.

That is, provided is a storage medium having a program stored thereon,the program including:

a code for making an image evaluation device perform a step of acquiringimage information to be evaluated;

a code for making the image evaluation device perform a step of settingan evaluation area for the acquired image information, the evaluationarea being smaller than an area corresponding to the image information;

a code for making the image evaluation device perform a step ofobtaining an individual evaluation on the acquired image information,the individual evaluation being obtained by individually evaluating apredetermined item in the set evaluation area;

a code for making the image evaluation device perform a step ofobtaining an other individual evaluation on the acquired imageinformation, the other individual evaluation being obtained byindividually evaluating an other predetermined item; and

a code for making the image evaluation device perform a step ofobtaining a comprehensive evaluation based on the individual evaluationand the other individual evaluation.

First Embodiment

<Apparatus to which the Image Evaluation Method is Applied>

The image evaluation method of acquiring image information to beevaluated and making evaluations on the image information acquired,thereby obtaining an evaluation result, can be applied to variousapparatuses. For example, this method may be applied to a personalcomputer on which a computer program for image evaluation is installed.This method may also be applied to a variety of apparatuses for handlingimage information. For example, it may be applied to photographicapparatuses for converting optical images of subjects into imageinformation, and printing apparatuses for printing images onto mediabased on image information. In this specification, description will begiven taking an ink-jet printer (hereinafter, referred to simply asprinter), a type of printing apparatus, for instance. More specifically,description will deal with a printer which can evaluate imageinformation to be printed, stored in a memory card or the like(corresponding to a first storage medium), and store the imageinformation and the corresponding evaluation results onto a CD-R or thelike (corresponding to a second storage medium).

Printer

<Configuration of Printer 1>

Initially, description will be given of the configuration of a printer1. Here, FIG. 1 is a diagram for explaining the appearance of theprinter 1 and a CD-R drive 100 connected to this printer 1. FIG. 2 is ablock diagram for explaining the electric configuration of the printer1.

As shown in FIG. 1, an operation panel 2, a liquid crystal displaysection 3, and a sheet discharge section 4 are arranged on the frontpart of the printer 1. The operation panel 2 is provided with varioustypes of operation buttons 5 and card slots 6. The operation buttons 5are operated when issuing commands to the printer 1. Moreover, the cardslots 6 are where a memory card 100 (a card-type flash memory, see FIG.2) is loaded into. This memory card 110 stores, for example, imageinformation on images that are shot by a digital camera 120.Incidentally, since there are several types of memory cards 110, aplurality of types of card slots 6 are also prepared so that therespective memory cards 110 can be loaded in. The liquid crystal displaysection 3 is a part for displaying various types of information. Thisliquid crystal display section 3 is where menus appear and images to beprinted are displayed. In the present embodiment, this liquid crystaldisplay section 3 is located above the operation buttons 5. The sheetdischarge section 4 is provided with a foldable catch tray. The catchtray is mounted so that its top part can be laid forward. Then, duringprinting, this catch tray functions as a stage for printed sheets(corresponding to a type of medium) to be put on. Meanwhile, a papersupply section 7 and various types of connectors (not shown) arearranged on the backside of the printer 1. The paper supply section 7 isa part capable of holding a stack of sheets to be printed. Theconnectors are parts for establishing connection with external devicessuch as the CD-R drive 100 and the digital camera 120.

In the example of FIG. 1, the CD-R drive 100 is connected to the printer1 via a cable 102. Incidentally, the connection to the printer 1 is notlimited to the cable 102, but may be made by wireless means. This CD-Rdrive 100 is used when backing up the image information stored in thememory card 110 into a CD-R 104. Moreover, when this CD-R drive 100 isconnected to the printer 1, it is possible to print image informationstored on a CD (compact disc, not shown) or the CD-R 104.

Next, description will be given of the electric configuration of theprinter 1. As shown in FIG. 2, the printer 1 has a head unit 20, a papercarrying mechanism 30, a carriage moving mechanism 40, a group ofdetectors 50, a printer-side controller 60, the liquid crystal displaysection 3, the operation buttons 5, and the card slots 6. Aside fromthese, the printer 1 also has a first interface (I/F) 12 forestablishing connection with a host computer 130, and a second interface14 for establishing connection with the digital camera 120 and the CD-Rdrive 100.

The head unit 20 is one for ejecting inks toward a sheet, and has a head22 for ejecting the inks and a head control section 24 for controllingthe head 22. This head unit 20 is attached to a carriage (not shown),and is moved in the carriage movement direction (a direction orthogonalto the carrying direction of paper; corresponding to a predetermineddirection) by the carriage moving mechanism 40. The paper carryingmechanism 30 corresponds to a medium carrying section for carrying amedium. This paper carrying mechanism 30 is one for feeding a sheet ofpaper, a medium, into a printable position, and carrying this sheet inthe carrying direction by predetermined amounts of carrying. Then, thispaper carrying mechanism 30 is composed of, for example, a sheet feedroller, a carrying roller, a carrying motor, and the like (none of whichis shown). The carriage moving mechanism 40 is one for moving thecarriage having the head unit 20 attached thereto in the carriagemovement direction. This carriage moving mechanism 40 is composed of,for example, a carriage motor, a timing belt, pulleys, and the like(none of which is shown). The group of detectors 50 is intended todetect the state of the printer 1. It includes, for example, a lineartype encoder for detecting the position of the carriage, a rotary typeencoder for detecting the amount of rotation of the carrying roller andthe like, and a sheet detector for detecting the presence or absence ofsheets (none of which is shown).

The printer-side controller 60 is one for controlling the printer 1.This printer-side controller 60 has a CPU 62, a memory 64, a drivesignal generating circuit 66, and a control unit 68. The CPU 62 is aprocessor for exercising overall control on the printer 1. Then, whenstoring image information stored in the memory card 110 or the like intothe CD-R 104, this CPU 62 makes evaluations on the target imageinformation, and attaches the obtained evaluations to the imageinformation to generate image information for storage (corresponding tothe image information intended for printing). The CPU 62 is thusequivalent to a controller for generating the image information forstorage. Incidentally, the evaluation and storage of the imageinformation will be detailed later. The memory 64 is intended to secureprogram-storing areas, work areas, and the like of the CPU 62, and iscomposed of memory elements such as RAMs, EEPROMs, and ROMs. Then, whenmaking evaluations on image information, the image information to beevaluated is stored in the memory 64. The drive signal generatingcircuit 66 is one for generating a drive signal for common use. Thedrive signal generated by this drive signal generating circuit 66 isapplied to piezoelectric elements (not shown) of the head 22. Then, thehead control section 24 controls the application of the drive signal tothe piezoelectric elements based on a head control signal sent from theCPU 62. The control unit 68 is interposed between the individualcomponents to be controlled and the CPU 62, generates drive signals formotors based on commands from the CPU 62, and converts and outputssignals sent from the components in a form interpretable to the CPU 62.

<Operation of the Printer 1>

Next, description will be given of the operation of this printer 1. Thisprinter 1 is characterized by the function of backing up imageinformation. Thus, the following description will detail the partspertaining to the backup of the image information, but state the otherparts in a brief way. Here, FIG. 3 is a flowchart for explaining themain processing of the printer 1. Incidentally, these processes areperformed by the CPU 62 based on a program stored in the memory 64. Forthis purpose, the computer program for operating the CPU 62 has codesfor realizing these processes.

Description will initially be given of the main processing. In this mainprocessing, a determination is initially made as to whether or not anymenu operation is made (S10). For example, in this processing, whetherany of the operating buttons 5 is operated or not is determined. Then,if none of the operation buttons 5 is operated, a determination is madewhether or not any print data is received from the host computer 130(S11). Here, if print data is received, printing based on the receivedprint data is executed (S12). That is, inks ejected from the head 22 arelanded on a sheet to form an image. If no print data is received or whenthe printing based on the print data is completed, the processingreturns to step S10 to determine whether or not any menu operation ismade.

Moreover, at step S10, if it is determined that there is some menuoperation, processing corresponding to the content of the operation isperformed. In this case, a determination is made as to whether or notthe content of the operation instructs to print image information storedin an external device (S20). Here, the external device shall refer to astorage medium separate from the printer 1, storing image information tobe printed. For example, it applies to the digital camera 120 and thememory card 110 loaded in a card slot 6. A CD or the CD-ROM 104 loadedinto the CD-R drive 100 is also applicable. Incidentally, imageinformation captured by the digital camera 120 is typically in JPEGformat, being compressed with a matrix of 8×8 pixels as a single block.Then, if the content of the operation instructs to print the imageinformation stored in the external device, the printing based on thisimage information is executed (S21).

Moreover, if the content of the operation does not pertain to theprinting of the image information stored in the external device, whetheror not it instructs to back up the image information is determined(S30). Then, if the content of the operation instructs to back up theimage information, processing for storing the image information storedin the memory card 110 (corresponding to the first storage medium) intothe CD-R 104 (corresponding to the second storage medium) is performed(S31). Incidentally, in the present embodiment, this backup processingincludes evaluating the image information. Then, the backup processingand the processing for evaluating the image information will be detailedlater. Moreover, if the content of the operation instructs otherprocessing, the processing corresponding to the content of instructionis performed (S40).

Evaluation and Backup of Image Information

<Overview of the Backup Processing>

Next, description will be given of the processing for evaluating imageinformation and the backup processing. Here, FIG. 4 is a flowchart forexplaining the evaluation and backup processing (S31) in the mainprocessing, and provides an overview of the processing.

In this processing, evaluation image information is acquired initially(S110). Here, the evaluation image information refers to imageinformation to be evaluated. In this processing, one piece of evaluationimage information is selected out of image information to be backed up,and this evaluation image information is stored into the memory 64.Thus, when there are a plurality of pieces of image information to bebacked up, one of them is acquired as the evaluation image information.Besides, when there is a single piece of image information to be backedup, that image information is acquired as the evaluation imageinformation. When the evaluation image information is acquired, imageevaluation processing is performed (S120). In this image evaluationprocessing, evaluations are made on a plurality of given items of theevaluation image information, respectively. Then, a comprehensiveevaluation is made from the plurality of evaluation results. It shouldbe noted that this image evaluation processing will be described later.

When the evaluations on the evaluation image information are made, adetermination is made as to whether or not all the image information hasbeen evaluated (S130). Here, if there is any image information yet to beevaluated, the procedure returns to step S110 to perform the processingdescribed previously. On the other hand, if all the image informationhas been evaluated, processing for confirming image information to bebacked up is performed (S140). In this processing, it is confirmed tothe operator whether or not to back up image information that does notsatisfy criteria, based on the evaluation results of the imageevaluation processing (S120). Then, through this confirmationprocessing, the image information to be backed up is fixed. It should benoted that this confirmation processing will also be described later.Then, when the image information to be backed up is fixed, the fixedimage information is written to the CD-R 104 (S150). In this writeprocessing, the evaluation results (the contents of evaluation) obtainedat step S120 are stored along with the image information that has beenevaluated. For example, the evaluation results are stored as Exif(exchangeable image file format) accessory information.

Image Evaluation Processing

Next, description will be given in detail of the image evaluationprocessing (S120) on the acquired evaluation image. Here, FIG. 5 is aflowchart for explaining the image evaluation processing.

The printer 1 of the present embodiment is characterized by this imageevaluation processing. That is, a predetermined item of the evaluationimage information is initially evaluated on an individual basis, therebyobtaining an individual evaluation. Next, another predetermined item isindividually evaluated to obtain another individual evaluation.Furthermore, a comprehensive evaluation is obtained based on theobtained individual evaluation and the other individual evaluations. Inshort, the characteristic consists in that a plurality of evaluationitems are evaluated individually, and a comprehensive evaluation isobtained from the individual evaluation results. Specifically, thisimage evaluation processing includes the processes of setting anevaluation area (S210), making a defocus evaluation (S220), making amis-focus evaluation (S230), making a motion blur evaluation (S240),making a block noise evaluation (S250), making a comprehensiveevaluation (S260), and storing evaluation results (S270). One of theprocesses of making a defocus evaluation (S220), a motion blurevaluation (S240), and a block noise evaluation (S250) corresponds tothe foregoing individual evaluation, and the two other processescorrespond to the other individual evaluations. Incidentally, themis-focus evaluation (S230) corresponds to an individual evaluation notto be included in the comprehensive evaluation (to be described later).

Hereinafter, description will be given of each of the processes.Incidentally, in the following description, the horizontal direction inan image will be referred to as X direction, and the vertical directionwill be referred to as Y direction. Moreover, the horizontal position ofa pixel P (see FIG. 6B and the like) has been idiomatically denoted by asymbol i, and the vertical position by a symbol j. Hence, pixels P willbe described by using these symbols i and j.

<Setting an Evaluation Area (S210)>

Initially, description will be given of the process for setting anevaluation area. Here, FIG. 6A is a conceptual diagram for explainingimage information which is divided into a predetermined number of blocksBK. FIG. 6B is a conceptual diagram for explaining differences inluminance between adjacent pixels. FIG. 6C is a conceptual diagramshowing an evaluation area EV which includes a block BK having a maximumnumber of edges. It should be noted that the evaluation area EV is thetarget area of the defocus evaluation (S220) and the motion blurevaluation (S240). In the present embodiment, a portion of the entirearea corresponding to the image information is set as an evaluation areaEV. This makes it possible to perform the processes of the defocusevaluation (S220) and the motion blur evaluation (S240) efficiently.Then, the evaluation area EV is set based on a block BK that has amaximum number of edges. This makes it possible to improve the accuracyof the defocus evaluation (S220) and the motion blur evaluation (S240).

When setting an evaluation area EV, the CPU 62 generates luminance imageinformation from the evaluation image information. Here, a luminanceimage is an image composed of luminance Y (information that indicatesbrightness), not including color information. The luminance imageinformation is information serving as a basis for this luminance image.In the present embodiment, the CPU 62 converts the evaluation imageinformation represented by RGB gradation values into image informationexpressed in a YIQ color space. Consequently, information on a Y channelfor indicating the luminances Y is acquired. Then, the Y-channelinformation acquired becomes the luminance image information, and animage that is displayed or otherwise processed based on the luminanceimage information becomes a luminance image.

Next, the CPU 62 divides the entire luminance image information into aplurality of rectangular blocks BK. In the present embodiment, as shownin FIG. 6A, it is divided into 256 blocks BK, or 16 equal partsvertically and 16 equal parts horizontally. Incidentally, forconvenience of illustration, FIG. 6A shows blocks BK in some of theentire area. After the division into the blocks BK, the CPU 62calculates differences in luminance between horizontally adjacent pixelsof the luminance image. Then, the absolute values of the differences inluminance are summed up for each of the blocks BK.

Suppose that a single block BK consists of 12 pixels from a top leftpixel P(i, j) to a bottom right pixel P(i+3, j+2) as shown in FIG. 6B.In this case, the CPU 62 subtracts the luminance Y(i, j) of the pixelP(i, j) from the luminance Y(i+1, j) of the pixel P(i+1, j) to determinea difference in luminance between these pixels. Similarly, the CPU 62subtracts the luminance (i+1, j) of the pixel P(i+1, j) from theluminance Y(i+2, j) of the pixel P(i+2, j) to determine a difference inluminance between these pixels. Calculations like this are conducted insuccession up to the pair of pixels P(i+3, j+2) and P(i+2, j+2). Then,the absolute values of the differences in luminance are summed up todetermine the total difference in luminance of that block BK in thehorizontal direction.

After the sums of the horizontal differences in luminance are calculatedfor the respective blocks BK, the CPU 62 performs the same processing inthe vertical direction of the luminance image. That is, differences inluminance between pixels adjacent in the vertical direction arecalculated, and the absolute values of the differences in luminance aresummed up for each of the blocks BK. With reference to the example ofFIG. 6B, differences in luminance between the pixels are calculated insuccession from the pair of pixels P(i, j+1) and P(i, j) to the pair ofpixels P(i+3, j+2) and P(i+3, j+1), and the absolute values of theluminances are summed up.

Then, when the sums of the differences in luminance (absolute values) ofall the blocks BK in the horizontal direction and the vertical directionare determined, the sums of the differences in the horizontal directionand the sums of the differences in the vertical direction are addedwithin the respective blocks BK, thereby determining the sum totals ofthe differences in luminance in the respective blocks BK. Then, theresulting sum totals of the differences in luminance are comparedbetween the individual blocks BK, thereby identifying the block BK thathas the highest sum total. Here, the sums of the differences inluminance in the horizontal direction and the vertical directions are inabsolute values. The block BK having the highest sum total ofdifferences in luminance is thus equivalent to a block BK that has themaximum differences in luminance when judged comprehensively in view ofthe pixels adjacent in the horizontal direction and the pixels adjacentin the vertical direction.

This block BK is considered to be a block BK having a maximum number ofedges. For example, in the image information of FIG. 6A, the area of thepersons' faces is considered to have largest differences in luminance.Consequently, the block BK corresponding to the portion of the persons'faces is identified as the block BK(max) that has the maximum sum totalof differences in luminance.

When the block BK(max) having the maximum sum total of differences inluminance is identified, the CPU 62 sets the evaluation area EV. Thisevaluation area EV is set to a position such that the block BK(max)having the maximum sum total of differences in luminance is located atthe center, and is set to a size into which the size corresponding tothe image information is reduced at a predetermined ratio. For example,as shown in FIG. 6C, it is set at a ratio of 0.25 (the size with whichthe size corresponding to the image information is divided into 16 equalparts). Since the evaluation area EV is thus set so that the block BKhaving the maximum differences in luminance is located at the center, itis possible to improve the accuracy of the defocus evaluation (S220) andthe motion blur evaluation (S240).

<Defocus Evaluation (S220)>

Now, description will be given of a defocus evaluation. Here, FIG. 7A isa diagram for explaining a Sobel filter in the X direction for use whengenerating an edge image. FIG. 7B is a diagram for explaining a Sobelfilter in the Y direction for use when generating an edge image. FIG. 7Cis a schematic diagram for explaining an area of 3×3 pixels with a givenpixel P(i, j) at the center, and the luminances Y of the pixels P inthis area. FIG. 8A is a diagram schematically explaining the applicationof the Sobel filters. FIG. 8B is a diagram schematically explaining agradient magnitude a (to be described later). FIG. 9A is a schematicdiagram for explaining an image having a sharp edge. FIG. 9B is a graphof the luminance Y corresponding to the edge of FIG. 9A. FIG. 9C is aschematic diagram for explaining an image having a blurred edge. FIG. 9Dis a graph of the luminance Y corresponding to the edge of FIG. 9C.

A defocus evaluation value indicates the degree of defocus. A defocusevaluation is made based on the widths of edges (hereinafter, alsoreferred to as edge widths WE). More specifically, edge widths WE areacquired from pixels P that have high probabilities of forming edges,and then the acquired edge widths WE are averaged (average edge widthWEav). Then, the average edge width WEav determined is normalized into adefocus evaluation value. Here, the defocus evaluation value is a kindof individual evaluation value, and is used when determining acomprehensive evaluation value. In terms of evaluation items, thedefocus evaluation thus corresponds to an individual evaluation to beincluded in a comprehensive evaluation. In the present embodiment, thedefocus evaluation value takes on integers of 0 to 10. For example, theshaper the focus is, the closer to “0” the value is. The higher thedegree of defocus is, the closer to “10” the value is.

This defocus evaluation is made on the image information in theevaluation area EV described above. For this purpose, the CPU 62initially converts the evaluation image information (RGB gradationvalues) into YIQ image information. Then, the CPU 62 determines anevaluation area EV of the information on the Y channel for indicatingthe luminance Y, and generates luminance image information in thisevaluation area EV. When the luminance image information in theevaluation area EV is obtained, the Sobel filter in the horizontaldirection (X direction) (FIG. 7A) and the Sobel filter in the verticaldirection (Y direction) (FIG. 7B) are applied to this luminance imageinformation. The Sobel filters are matrixes having 3×3, nine elements.The application of such Sobel filters produces edge gradients dx and dy(horizontal edge gradients dx and vertical edge gradients dy). In otherwords, an image that shows pixels P making large changes in luminance Yin the horizontal direction and an image that shows pixels P makinglarge changes in luminance Y in the vertical direction are obtained. Inshort, a horizontal edge gradient image and a vertical edge gradientimage are obtained.

Here, brief description will be given of the application of Sobelfilters. For example, applying a Sobel filter to a certain pixel P(i, j)shown in FIG. 7C means that the products of the luminances Y(i−1, j−1)to (i+1, j+1) of the 3×3 pixels P lying in the vicinity of that pixel Pand the respective corresponding elements of the Sobel filter arecalculated, and the nine products determined are summed up. When theSobel filer in the X direction is applied to the pixel P(i, j), the edgegradient dx(i, j) resulting from the application is given by thefollowing equation (1):dx(i, j)=[Y(i+1, j−1)+2×Y(i+1, j)+Y(i+1, j+1)]−[Y(i−1, j−1)+2×Y(i−1,j)+Y(i−1, j+1)]  (1)

As a result of application of the Sobel filter in the X direction andthe Sobel filter in the Y direction, the horizontal edge gradient dx andthe vertical edge gradient dy of the given pixel P(i, j) are determinedfrom this pixel P(i, j) and the eight pixels P surrounding this pixelP(i, j) as shown hatched in FIG. 8A. When the edge gradients dx and dyare determined, the CPU 62 determines the gradient magnitude a(i, j) andthe edge direction θ(i, j) for each of the pixels P within theevaluation area EV. Here, the gradient magnitude a(i, j) corresponds tothe degree of being an edge. This gradient magnitude a(i, j) isexpressed as the magnitude of the vector shown in FIG. 8B, and iscalculated based on the following equation (2), for example. Moreover,the edge direction θ(i, j) is the direction determined by the ratiobetween the horizontal edge gradient dx and the vertical edge gradientdy. That is, as shown in FIG. 8B, the edge direction θ(i, j) is thedirection of the sum of the vector of the edge gradient dx and thevector of the edge gradient dy, and is generally orthogonal to a linethat is composed of a plurality of adjoining edges.a(i, j)=√{square root over (dx(i, j)² +dy(i, j)²)}{square root over(dx(i, j)² +dy(i, j)²)}  (2)

When the gradient magnitudes a(i, j) and the edge directions θ(i, j) aredetermined, the CPU 62 classifies the pixels P into horizontal edges andvertical edges based on the respective edge directions θ. Then, the edgewidths WE are calculated in the classified directions. In the presentembodiment, the edge of a pixel P is classified as horizontal when theedge direction θ is closer to the horizontal direction. The edge of thepixel P is classified as vertical when the edge direction θ is closer tothe vertical direction. Specifically, an edge is classified ashorizontal when the edge direction θ is below 45°, and is classified asvertical when the edge direction θ is from 45° to 90°. Then, the CPU 62determines the edge widths WE(i, j) in the classified directions. Here,an edge width WE(i, j) is the distance (i.e., the number of pixels) froma pixel P that is the first to show a maximum luminance Y to a pixel Pthat is the first to show a minimum luminance Y, being determined withinan area including the target pixel P(i, j). For example, the image ofFIG. 9A has a sharp edge, and the edge width WE1 is determined to have asufficiently small value. On the other hand, the image of FIG. 9C has ablurred edge, and the edge width WE2 is determined to have a valuegreater than the edge width WE1.

After the edge widths WE of the respective pixels P in the evaluationarea EV are determined in this way, the CPU 62 sums up the determinededge widths WE to calculate a total edge width WEa (=ΣWE). Then, thistotal edge width WEa is divided by the total number of edges Ne todetermine an average edge width WEav (=WEa/Ne) per edge. Then, based onthe average edge width WEav determined, a defocus evaluation value of“0” to “10” is acquired. For example, possible maximum and minimumvalues of the average edge width WEav are determined and the range fromthe minimum value to the maximum value is equally divided to create acorrelation table between the average edge width WEav and the defocusevaluation value. This correlation table is stored in the memory 64, forexample, and an average edge value WEav determined is applied to thecorrelation table to acquire a defocus evaluation value.

<Mis-Focus Evaluation (S230)>

Next, description will be given of a mis-focus evaluation. Here, FIG. 10is a diagram schematically showing classified blocks BK2. Moreover, FIG.11 is a schematic diagram showing stepwise how the blocks BK2 arereclassified by object division processing.

In this mis-focus evaluation, a comprehensive mis-focus evaluation valueMS is determined. This comprehensive mis-focus evaluation value MS showsthe degree of mis-focus of the evaluation image. Here, mis-focus refersto the state that an area different from an area intended for shootingis in focus. Hence, the comprehensive mis-focus evaluation value MSindicates the degree of focus on an area different from an area intendedfor shooting. Incidentally, this comprehensive mis-focus evaluationvalue MS is not used for a comprehensive evaluation (S260). The reasonis that whether mis-focus or not depends largely on the subjective viewof the photographer. Consequently, in terms of evaluation items,mis-focus corresponds to a nontarget item not to be included in thecomprehensive evaluation.

A mis-focus evaluation is made on the entire evaluation imageinformation. For this purpose, the CPU 62 obtains luminance imageinformation from YIQ information which is obtained by converting theevaluation image information. When the luminance image information isobtained, the CPU 62 applies the horizontal Sobel filter (FIG. 7A) andthe vertical Sobel filter (FIG. 7B) to this luminance image information,thereby obtaining a horizontal edge gradient image and a vertical edgegradient image. When these edge gradient images are obtained, themagnitudes a(i, j) of the edge gradients and the edge directions θ(i, j)are determined based on the edge gradients dx and dy. Next, the CPU 62classifies the pixels P into horizontal edges and vertical edges basedon the edge directions θ determined. Then, the edge widths WE in theclassified directions are calculated. Incidentally, since theseprocesses are the same as the foregoing, description thereof will beomitted.

Next, based on the magnitudes a(i, j) of the edge gradients and the edgewidths WE(i, j), the CPU 62 determines pixel mis-focus evaluation valuesM(i, j) which indicate the degrees of mis-focus of the respective pixelsP. As shown by the following equation (3), these pixel mis-focusevaluation values M(i, j) are determined by the ratios between the edgewidths WE(i, j) and the magnitudes a(i, j) of the edge gradients. Thus,the greater the magnitudes a(i, j) of the edge gradients are, thesmaller the values of the pixel mis-focus evaluation values M(i, j) are.The smaller the edge widths WE(i, j) are, the smaller the values of thepixel mis-focus evaluation values M(i, j) are. This shows that theclearer the edges are, the smaller values the pixel mis-focus evaluationvalues M(i, j) have.M(i, j)=we(i, j)/a(i, j)  (3)

Incidentally, in the present embodiment, when the magnitude a(i, j) ofan edge gradient is approximately “0” in value, the pixel mis-focusevaluation value M(i, j) shall be marked with a symbol (for example,asterisk) for indicating that the evaluation is not available. Forexample, evaluations might become unavailable in areas where theluminance Y changes little. Specifically, pixels P of sky, sea, and thelike would apply.

After the pixel mis-focus evaluation values M(i, j) of the respectivepixels P are calculated, the CPU 62 divides the luminance imageinformation into m×n blocks BK2. Here, m and n are arbitrary integers.Then, the blocks BK2 for this processing may be identical to the blocksBK for the processing of setting an evaluation area EV (S210), or may bedefined independently. After the division into the blocks BK2, the CPU62 acquires mis-focus evaluation values block by block (forconvenience's sake, also referred to as block mis-focus evaluationvalues M2). These block mis-focus evaluation values M2(m, n) areacquired by extracting maximum values out of the pixel mis-focusevaluation values M(i, j) of the pixels P in the respective blocks BK2.

Next, the CPU 62 classifies the blocks BK2 into mis-focus blocks BK2(M)which are not in focus, and in-focus blocks BK2(I) which are in focus.This classification is achieved by comparing the block mis-focusevaluation values M2(m, n) with a threshold value for blockclassification. To explain in concrete terms, the CPU 62 reads the blockmis-focus evaluation values M2(m, n) and the threshold value for blockclassification from the memory 64. If block mis-focus evaluation valuesM2(m, n) are greater than the threshold value for block classification,the CPU 62 classifies those blocks BK2 as mis-focus blocks BK2(M). Onthe other hand, if smaller than the threshold value for blockclassification, those blocks BK2 are classified as in-focus blocksBK2(I). By this classification, block classifications (m, n) areobtained. For example, as shown in FIG. 10, the obtained blockclassifications (m, n) include mis-focus blocks BK(M), in-focus blocksBK2(I), and evaluation-unavailable blocks BK2(O). Incidentally, theevaluation-unavailable blocks BK2(O) refer to blocks BK2 in which thepixel mis-focus evaluation values M(i, j) of all the pixels P pertainingto those blocks BK2 show that the evaluations are unavailable.

Next, the CPU 62 performs object division processing. This objectdivision processing is processing for padding discrete in-focus blocksBK2(I) and mis-focus blocks BK2(M) with peripheral blocks so that thesame types of blocks are arranged continuously. In the presentembodiment, this object division processing is performed based on Bayes'theorem. To give a brief explanation, the CPU 62 calculates posteriorprobabilities for situations where the individual blocks BK2 aremis-focus blocks BK2(M) and posterior probabilities for situations within-focus blocks BK2(I), in consideration of the classifications of theadjacent blocks BK2. Next, the CPU 62 updates the block classificationswith those having higher posterior probability calculations. Then, asshown in FIG. 11, the processing of calculating the posteriorprobabilities and the processing of updating the block classificationsare repeated until all the blocks BK2 are classified into either ofin-focus blocks BK2(I) and mis-focus blocks BK2(M).

After the object division processing is performed, the CPU 62 obtains amis-focus evaluation value on the evaluation image information (forconvenience's sake, also referred to as comprehensive mis-focusevaluation value MS) based on the in-focus blocks BK2(I) and themis-focus blocks BK2 (M). This comprehensive mis-focus evaluation valueMS takes on integers of 0 to 10, for example. For example, the lower thepossibility of mis-focus an evaluation image has, the closer to “0” thevalue is. The higher the possibility of mis-focus is, the closer to “10”the value is. Then, this comprehensive mis-focus evaluation value MS isdetermined based on the numbers of in-focus blocks BK2(I) and mis-focusblocks BK2(M), the ratio between the in-focus blocks BK2(I) and themis-focus blocks BK2(M), the positions of the mis-focus blocks BK2(M) inthe evaluation image information, and so on. These criteria can beselected in various ways. In this example, the higher proportion themis-focus blocks BK2(M) occupy the evaluation image information at, thehigher the comprehensive mis-focus evaluation value MS is made.Moreover, the smaller the number of sides the in-focus blocks BK2(I) arein contact with, out of the top, bottom, right, and left four sides ofthe evaluation image information, the higher the comprehensive mis-focusevaluation value MS is made. Incidentally, these criteria are just a fewexamples, and other criteria may also be employed.

<Motion Blur Evaluation (S240)>

Next, description will be given of a motion blur evaluation. Here, FIG.12 is a schematic diagram for explaining the relationship between thedirection of edge intensity (θmax) and the direction of a motion blur(θmax+π/2). FIG. 13 is a diagram for explaining pixels P adjoining inone direction, and a function f(n) obtained from the luminances Ycorresponding to the respective pixels P. FIG. 14 is a schematic diagramfor explaining an autocorrelation function.

In this motion blur evaluation, a motion blur evaluation value isdetermined. This motion blur evaluation value indicates the degree of amotion blur in an evaluation image, and is used when obtaining acomprehensive evaluation. Thus, in terms of evaluation items, the motionblur evaluation corresponds to an individual item to be included in thecomprehensive evaluation.

Initially, an overview will be given of the motion blur evaluation. Amotion blur refers to a phenomenon that occurs from the movement of thecamera during exposure so that the subject is shifted in the directionof movement of the camera. Thus, the motion blur evaluation in thepresent embodiment indicates the degree of shift of the subject.Initially, the direction of a motion blur (θmax+π/2) is determined, anda function f(n) between pixels P and luminances Y is obtained in thedirection of the motion blur determined. Then, an autocorrelationfunction ACFk(τ) given by the following equation (4) is determined as tothe obtained function f(n). The displacement τ at which thisautocorrelation function ACFk(τ) takes the minimum value is regarded asthe amount of the motion blur.

$\begin{matrix}{{{{ACFK}(\tau)} = {\frac{1}{N}{\sum\limits_{n = 0}^{N - 1}{f(n)}}}}{\cdot {f\left( {n + \tau} \right)}}} & (4)\end{matrix}$

That is, when a motion blur occurs, a given point on a subject iscaptured as a line across the range corresponding to the amount of themotion blur. Since this line is an identical point on the subject, ithas high correlativeness in color. The amount of the motion blur canthus be acquired by determining the function f(n) between pixels P andluminances Y in the direction of the motion blur, and calculating theautocorrelation function ACFk(τ) while shifting the pixel P in thedetermined function f(n) in the direction of the motion blur. In short,the displacement τ at which the autocorrelation function ACFk(τ) reachesits minimum is considered to be the amount of the motion blur. Then, theCPU 62 determines the motion blur evaluation value from the amount ofthe motion blur acquired. This motion blur evaluation value is thenormalized amount of the motion blur. That is, the amount of the motionblur is normalized into a motion blur evaluation value. Here, like thedefocus evaluation value, the motion blur evaluation values also takeson integers of 0 to 10. For example, the smaller the amount of themotion blur is, the closer to “0” the value is. The greater the amountof the motion blur is, the closer to “10” the value is.

Next, the motion blur evaluation will be described in the concrete. Thismotion blur evaluation is also made based on the luminances Y in theforegoing evaluation area EV. For this reason, the CPU 62 obtains theluminance image information in the evaluation area EV from the YIQinformation that is based on the evaluation image information. Next, theCPU 62 applies the horizontal Sobel filter (FIG. 7A) and the verticalSobel filter (FIG. 7B) to the luminance image information, therebyobtaining a horizontal edge gradient image dx and a vertical edgegradient image dy. When the edge gradient images dx and dy are obtained,a determination is made as to which angle the edges appear most clearlyat in the evaluation area EV. Here, as shown in FIG. 12, the CPU 62calculates edge intensities E(n) based on the following equation (5) atrespective determination angles θn (n: 1 to 32) on a ½ circle. Thisequation (5) shows that an edge intensity E(n) is expressed by the sumof the absolute values (ABS) of cos θ·dx+sin θ·dy, and thus is the totalsum of the edge gradients in the determination angle θn.

$\begin{matrix}{{E(n)} = {\sum\limits_{j}{\sum\limits_{i}\left\lbrack {{ABS}\left( {{\cos\;\theta\;{n \cdot {{dx}\left( {i,j} \right)}}} + {\sin\;\theta\;{n \cdot {{dy}\left( {i,j} \right)}}}} \right)} \right\rbrack}}} & (5)\end{matrix}$

Then, based on the edge intensities E(n) determined, an angle θmax withthe highest edge intensity is acquired. This angle θmax can be said tobe the direction in which edges appear the most. Supposing that a motionblur has occurred during exposure, edges in the same direction as thatof the motion blur are shifted in the same direction as that of themotion blur, thereby becoming blurred. Edges in the directionperpendicular to the direction of the motion blur, however, tend toremain as edges even when shifted in the same direction as that of themotion blur. This means that the angle θmax with the highest edgeintensity is the direction perpendicular to that of the motion blur.Consequently, the direction θmax+π/2 perpendicular to this angle θmaxcan be said to be the same angle as the direction of the motion blur.

When the direction of the motion blur is thus acquired, the CPU 62, asshown in FIG. 13, determines a group of pixels that are in the directionof the motion blur, and determines a function f(n) of the luminance Yfrom the group of pixels. Next, based on the foregoing equation (4), theCPU 62 determines a autocorrelation function ACFf(τ) between thisfunction f(n) and the function f(n+τ) that is shifted by a displacementτ.

That is, the CPU 62 determines the displacement τ at which theautocorrelation function ACFf(τ) reaches its minimum as the amount ofthe motion blur. As can be seen from the equation (4), theautocorrelation function ACFk(τ) is obtained by calculating the productof the original function f(n) and the function f(n+τ), which is afunction shifted by a displacement τ, for each of N pixels (i.e., pixelsn=0 to N−1), and averaging the same (1/N). Assuming here that thedisplacement τ is zero, the autocorrelation function ACFk(τ) reaches itsmaximum since the pixels of the function f(n) coincide with the pixelsof the function f(n+τ), respectively. Then, as the displacement τ isincreased like 1, 2, . . . , the function f(n+τ) deviates from thefunction f(n). This lowers the degree of coincidence between the pixelsof the function f(n) and the pixels of the function f(n+τ). As a result,the autocorrelation function ACFk(τ) also decreases each time thedisplacement τ increases. Then, when a displacement τ corresponding tothe amount of the motion blur is set, as shown by the dotted line inFIG. 13, the autocorrelation function ACFk(τ) takes its minimum valuesince the function f(n+τ) and the function f(n) do not overlap anylonger. If the displacement τ is increased further, the correlationbetween the function f(n) and the function f(n+τ) disappears, so thatthe autocorrelation function ACFk(τ) can exhibit higher values, butunstable, than the foregoing minimum value.

For example, as shown in FIG. 14, when a motion blur occurs over threepixels in the horizontal direction, the luminances Y of those pixels,from a pixel P(i, j) to the third pixel P(i+3, j) from that pixel, showsimilar values. In this case, if a displacement τ of four (pixels) isgiven, the blurred portion of the function f(n) and the blurred portionof the function f(n+τ) no longer overlap with each other, and theautocorrelation function ACFf(τ) takes a minimum value. Consequently,the displacement τ that yields the lowest value of the autocorrelationfunction ACFf(τ) can be said to indicate the amount of the motion blur.

Incidentally, for the sake of improved accuracy in detecting the amountof the motion blur, a plurality of sample lines may be determined sothat the amount of a motion blur is acquired based on theautocorrelation functions ACFk(τ) determined on the respective samplelines. This makes it possible to acquire the amount of a motion bluraccurately in such cases that the motion blur occurs along a circulartrack.

When the amount of the motion blur is thus determined, the CPU 62acquires a motion blur evaluation value of “0” to “10” based on theamount of the motion blur determined. For example, possible maximum andminimum values of the amount of a motion blur are determined, and therange from the minimum value to the maximum value is equally divided tocreate a correlation table between the amount of a motion blur and themotion blur evaluation value. This correlation table is stored in thememory 64, for example, and the amount of a motion blur determined isapplied to the correlation table to acquire a motion blur evaluationvalue.

<Block Noise Evaluation (S250)>

Next, description will be given of a block noise evaluation. Here, FIG.15 is an explanatory diagram showing an example of luminance Y,differences in the magnitude of luminance Y, and the smoothness ofchanges in luminance Y.

Initially, description will be given of block noise. Block noise refersto differences in luminance Y that can occur from block borders due toJPEG compression. Incidentally, as employed here, a “block” refers toeach single unit of JPEG compression. In the block noise evaluation, ablock noise evaluation value is determined. This block noise evaluationvalue indicates the degree of noise occurring from block borders, i.e.,the degree of noise occurring at compression time. For example, theblock noise evaluation value takes on integers of 0 to 10. For example,the lower degree of block noise the evaluation image information has,the closer to “0” the value is. The higher the degree is, the closer to“10” the value is. This block noise evaluation value is also used whenobtaining a comprehensive evaluation. Thus, in terms of evaluationitems, block noise corresponds to an individual item to be included inthe comprehensive evaluation.

A block noise evaluation is made on the entire evaluation imageinformation. For this reason, the CPU 62 obtains the luminance imageinformation from the YIQ information that is based on the evaluationimage information. When the luminance image information is obtained, theCPU 62 calculates differences in luminance between individual pixels inthe horizontal direction and the vertical direction from this luminanceimage information. Since this method for calculating differences inluminance is the same as the one described in the setting of anevaluation area (S210), description thereof will be omitted. When thedifferences in luminance are determined, the smoothness of changes inthe luminances Y of the respective pixels P in the horizontal directionand the smoothness of changes in the luminances Y of the respectivepixels P in the vertical direction are calculated based on luminancedifferences ddx in the horizontal direction and luminance differencesddy in the vertical direction. Here, the smoothness of changes in theluminances Y of pixels P in the horizontal direction shall berepresented by horizontal smoothness sx(i, j), and the smoothness ofchanges in the luminances Y of pixels P in the vertical direction shallbe represented by vertical smoothness sy(i, j).

The horizontal smoothness sx(i, j) is given by the following equation(6), and the vertical smoothness sy(i, j) is given by the followingequation (7). As can be seen from these equations (6) and (7), thesmoothnesses sx and sy among adjoining three pixels P are each expressedas the sum of the absolute value of a value determined by subtractingthe luminance difference of the second pixel P from the luminancedifference of the first pixel P, and the absolute value of a valuedetermined by subtracting the luminance difference of the third pixel Pfrom the luminance difference of the second pixel P. After thesmoothnesses sx and sy are calculated, the CPU 62 calculates an averagehorizontal smoothness ave (psx) and an average vertical smoothnessave(psy) of the pixels P that fall on the borders of the blocks used inJPEG compression (hereinafter, also referred to as border pixels). TheCPU 62 also calculates an average horizontal smoothness ave (nsx) and anaverage vertical smoothness ave (nsy) of the pixels P other than theborder pixels (hereinafter, also referred to as inner pixels).

$\begin{matrix}{{{sx}\left( {i,j} \right)} = {{{ABS}\left\lbrack {{{ddx}\left( {{i - 1},j} \right)} - {{ddx}\left( {i,j} \right)}} \right\rbrack} + {{ABS}\left\lbrack {{{ddx}\left( {i,j} \right)} - {{ddx}\left( {{i + 1},j} \right)}} \right\rbrack}}} & (6) \\{{{sy}\left( {i,j} \right)} = {{{ABS}\left\lbrack {{{ddy}\left( {i,{j - 1}} \right)} - {{ddy}\left( {i,j} \right)}} \right\rbrack} + {{ABS}\left\lbrack {{{ddy}\left( {i,j} \right)} - {{ddy}\left( {i,{j + 1}} \right)}} \right\rbrack}}} & (7)\end{matrix}$

For example, the CPU 62 calculates an average horizontal smoothnessave(psx) of the border pixels by dividing the total sum of sx(x, y) bythe number of the same, where x is multiples of 8. The CPU 62 alsocalculates an average vertical smoothness ave(psy) of the border pixelsby dividing the total sum of sx(x, y) by the number of the same, where yis multiples of 8. Moreover, the CPU 62 makes similar calculations onthe inner pixels. For example, the CPU 62 calculates an averagehorizontal smoothness ave(nsx) of changes in the luminances Y of theinner pixels by dividing the total sum of sx(x, y) by the number of thesame, where x is other than the multiples of 8. The CPU 62 alsocalculates an average vertical smoothness ave(nsy) of changes in theluminances Y of the inner pixels by dividing the total sum of sx(x, y)by the number of the same, where y is other than the multiples of 8.

Then, a block noise evaluation value Bh in the horizontal direction iscalculated as the ratio of the average horizontal smoothness ave(psx) ofthe border pixels to the average horizontal smoothness ave(nsx) of theinner pixels [ave (psx)/ave (nsx)]. Similarly, a block noise evaluationvalue Bv in the vertical direction is calculated as the ratio of theaverage horizontal smoothness ave(psy) of the border pixels to theaverage horizontal smoothness ave(nsy) of the inner pixels[ave(psy)/ave(nsy)]. Furthermore, a block noise evaluation value B ofthe evaluation image information is determined by averaging the blocknoise evaluation value Bh in the horizontal direction and the blocknoise evaluation value Bv in the vertical direction. In the presentembodiment, the determined value is normalized in the range of 0 to 10,thereby obtaining the block noise evaluation value B. That is, an imagewithout any block noise has a value of 0, and an image having a maximumpossible level of block noise has a value of 10.

<Comprehensive Evaluation (S260)>

Next, description will be given of a comprehensive evaluation. Here,FIG. 16 is a conceptual diagram showing the correlation between adetermination factor and a comprehensive evaluation value, showing avariation by weighting. FIG. 17 is a conceptual diagram showing thecomprehensive evaluation value when determined based on the defocusevaluation value and the motion blur evaluation value.

A comprehensive evaluation refers to a composite evaluation on imageinformation, being made based on evaluations on two or more items. Inthis comprehensive evaluation, the items are given respective weights.That is, heavily-weighted items have higher impact on the comprehensiveevaluation than lightly-weighted items do. In the present embodiment,the weighting is based on a Gaussian function. This Gaussian function isgiven by the following equation (8), and generally shows a normaldistribution.

$\begin{matrix}{\mspace{85mu}{q_{o\;\upsilon} = {k{\prod\limits_{n = 1}^{N}\;{\exp\left\{ {- \frac{q_{n}^{2}}{2 \times \;\upsilon_{n}^{2}}} \right\}}}}}} & (8) \\{\mspace{85mu}{q_{1} = {1 - \frac{C_{1}}{10}}}} & (9) \\{\mspace{85mu}{q_{2} = {1 - \frac{C_{2}}{10}}}} & (10) \\{\mspace{85mu}{q_{3} = {1 - \frac{C_{3}}{10}}}} & (11) \\{q_{o\;\upsilon} = {{k \times \exp}{\left\{ {- \frac{\left( {1 - \frac{C_{1}}{10}} \right)^{2}}{2 \times \upsilon_{1}^{2}}} \right\} \times \exp}{\left\{ {- \frac{\left( {1 - \frac{C_{2}}{10}} \right)^{2}}{2 \times \upsilon_{2}^{2}}} \right\} \times \exp}\left\{ {- \frac{\left( {1 - \frac{C_{3}}{10}} \right)^{2}}{2 \times \upsilon_{3}^{2}}} \right\}}} & (12)\end{matrix}$

In this equation (8), q_(n) are determination factors which areequivalent to a defocus evaluation factor q₁, a motion blur evaluationfactor q₂, and a block noise evaluation factor q₃. Here, thedetermination factors q_(n) are normalized in the range of “0” to “1”.The closer to “0,” the lower image quality they indicate. The closer to“1,” the higher image quality they indicate. Thus, the closer to “0” thedefocus evaluation factor q₁ is, the more it indicates the image is outof focus. The closer to “1,” the more it indicates the image is infocus. With the motion blur evaluation factor q₂, the closer to “0,” thegreater it indicates the motion blur is. The closer to “1,” the smallerit indicates the motion blur is. With the block noise evaluation factorq₃, the closer to “0,” the higher it indicates the block noise is. Thecloser to “1,” the lower it indicates the block noise is. Then, thedefocus evaluation factor q₁, the motion blur evaluation factor q₂, andthe block noise evaluation factor q₃ are determined from the foregoingdefocus evaluation value (C1), motion blur evaluation value (C2), andblock noise evaluation value (C3) through the conversion processing ofthe equations (9) to (11). Based on these equations (9) to (11), theequation (8) can be expressed as the equation (12).

In the equation (8), V_(n) are weighting factors. Then, as shown in theequation (12), these weighting factors V_(n) are equivalent to aweighting factor V₁ for a defocus evaluation, a weighting factor V₂ fora motion blur evaluation, and a weighting factor V₃ for a block noiseevaluation, and are set at values corresponding to the respective items.In the present embodiment, the weighting factor V₁ for a defocusevaluation is 0.5, the weighting factor V₂ for a motion blur evaluationis 0.3, and the weighting factor V₃ for the block noise evaluation is0.4. The squares of these weighting factors V_(n) are equivalent tovariances σ² in a normal distribution. Consequently, as shown by thesolid line in FIG. 16, the greater a weighting factor V_(n) is, thegentler the curve rises. Moreover, as shown by the dotted line, thesmaller the weighting factor V_(n) is, the sharper the curve rises. Thismeans that the greater the weighting factor V_(n) is, the higher theimpact on the comprehensive evaluation is. For example, given twofactors or the defocus evaluation factor q₁ and the motion blurevaluation factor q₂, a comprehensive evaluation value q_(ov) isdetermined depending on the respective weights, as shown in FIG. 17.

Based on the same concept, in the present embodiment, the comprehensiveevaluation value q_(ov) is determined by the three factors. Here, theweighting factor V₁ for a defocus evaluation is the greatest, and theweighting factor V₃ for a block noise evaluation is the second.Moreover, the weighting factor V₂ for a motion blur evaluation is thesmallest. The reason for this is that the impact of defocus on imagequality is typically higher than that of block noise on image quality.It is also because the impact of block noise on image quality is higherthan that of a motion blur on image quality. Then, the CPU 62 performsthe computing of the equation (12) to calculate the comprehensiveevaluation value. That is, the comprehensive evaluation value q_(ov) isdetermined from the defocus evaluation factor q₁, the motion blurevaluation factor q₂, and the block noise evaluation factor q₃.

According to the comprehensive evaluation value q_(ov) determined thus,it is possible to evaluate the evaluation image appropriately. Thereasons for this are as follows. First, when the comprehensiveevaluation is made from the plurality of evaluation items, the items aregiven their respective weights. This makes it possible to make anappropriate evaluation. Second, the weights on the respective items areassigned based on the impacts on image quality. That is, since the itemshaving higher impacts on image quality are given higher weights, it ispossible to determine the comprehensive evaluation value that shows amore appropriate evaluation. Third, the comprehensive evaluation valueq_(ov) is determined by using the Gaussian function. That is, since thecomprehensive evaluation value is determined by using the Gaussianfunction, the comprehensive evaluation value can be calculated with highprecision based on the evaluation values of the items and the weightsassigned to the respective items.

Besides, in the present embodiment, an evaluation is also made on theitem that is not used when determining the comprehensive evaluationvalue q_(ov) (nontarget item), or namely, on mis-focus. Since theevaluation is also made on the item that is not used when calculatingthe comprehensive evaluation value, it is possible to provide detailedevaluations to the operator.

<Storing the Evaluation Results (S270)>

Next, the CPU 62 stores the obtained evaluation results into the memory64 in association with the evaluation image information. Here, the CPU62 stores the defocus evaluation value, the comprehensive mis-focusevaluation value MS, the motion blur evaluation value, the block noiseevaluation value, and the comprehensive evaluation value, accompaniedwith information for establishing association with the evaluation imageinformation, into the memory 64. For example, information for describingthe filename of the evaluation image information is attached forstorage. Then, after the evaluation results are stored, the CPU 62repeats the processes described above until all the images to be backedup are evaluated (S130).

Confirmation of Backup Information

Next, description will be given of the processing for confirming theimage information to be backed up (S140). Here, FIG. 18 is a flowchartfor explaining this confirmation processing.

This confirmation processing is performed after the image evaluationprocessing (S120). Then, in this confirmation processing, it isconfirmed to the user whether or not to back up image information whosecomprehensive evaluation value determined is lower than or equal to acriterion value. Here, image information that is confirmed to be backedup is stored into a storage medium such as the CD-R 104, along with theimage information whose comprehensive evaluation value exceeds thecriterion value (S150).

To make such a confirmation, processing for extracting image informationunder the criterion value is initially performed in the confirmationprocessing (S310). In this processing, the CPU 62 refers to theevaluation results stored in the memory 64, and compares the referredevaluation results and the criterion value. Then, image informationequal to or below the criterion value is extracted. In the presentembodiment, the CPU 62 refers to the comprehensive evaluation value ofeach piece of image information. Then, if this comprehensive evaluationvalue is lower than or equal to, e.g., “6,” then that piece of imageinformation is extracted as one equal to or below the criterion value.It should be noted that the result of extraction is filenameinformation, for example, and is stored into the memory 64.

Next, the image information equal to or below the criterion value isdisplayed (S320). Here, in the present embodiment, if there are aplurality of pieces of image information equal to or below the criterionvalue, only one of the pieces of image information is displayed. Thereason for this is that the liquid crystal display section 3 provided onthe printer 1 has a limited size, and a thumbnail view can thus makeimages difficult to identify. In this case, the order of display may bedetermined arbitrarily. In the present embodiment, images are displayedin order of filename. In general, pieces of image information shot bythe digital camera 120 are given serial filenames. Displaying in orderof filename is thus advantageously easy for the operator to understand.

When the image information is displayed, a confirmation from theoperator is waited (S330). Here, the CPU 62 monitors a signal from theoperating section, and determines the presence or absence of aconfirmation operation based on this signal. The CPU 62 also identifieswhether or not to back up the image information, based on the signalfrom the operating section. In other words, it acquires confirmationinformation indicating whether or not to back up. Then, when theconfirmation information is acquired, the CPU 62 updates a list of imageinformation to be backed up based on this confirmation information.Then, the CPU 62 checks for image information yet to be confirmed(S340), and if there is image information yet to be confirmed, returnsto the processing of step S320 to perform the same processing on thenext piece of image information. Moreover, if confirmation has been madeon all the image information extracted, the CPU 62 fixes the imageinformation to be backed up (S350).

When the image information to be backed up is fixed, the fixed imageinformation is written to the CD-R 104 (S150). Here, the CPU 62 attachesthe information on the individual evaluation values obtained by theimage evaluation processing to the image information to be backed up,thereby generating image information intended for writing (correspondingto the image information for storage). In the present embodiment,comprehensive evaluation values, as well as defocus evaluation values,motion blur evaluation values, and noise block evaluation values whichare used to determine the same, are attached to the image information.In addition, comprehensive mis-focus evaluation values MS, which are notused when determining the comprehensive evaluation values, are alsoattached. For example, when the image information is Exif-compatible,these pieces of information are stored as Exif accessory information.Moreover, if not Exif-compatible, these pieces of information are storedin the form of attached files of the image information. Then, thegenerated image information for writing is transferred to the CD-R drive100 via the cable 102 or the like, and stored into the CD-R 104. Then,step S150 for performing such processing is equivalent to the evaluationattachment step and another evaluation attachment step.

Use of Image Information Backed Up

Next, description will be given of the use of the image informationbacked up to the CD-R 104. Here, FIG. 19 is a diagram for explaining anexample of display of the image information backed up. This displayexample is of an image display software application which runs on apersonal computer. In the display example shown in FIG. 19, a displayarea for evaluation items is arranged below a display area for imageinformation. The display area for evaluation items is divided into rightand left. The individual evaluations are displayed on the left area, anda comprehensive evaluation and a comment appear on the right area. Sincethe individual evaluations and the comprehensive evaluation aredisplayed in this way, the user of this image display softwareapplication can be informed of the objective evaluation on this image.Then, this evaluation can be used as auxiliary information when making adetermination on printing, correction, etc. Moreover, when the imageinformation is displayed as a thumbnail image, it is preferable to popup a display image for evaluation items when the mouse cursor or thelike is put on the thumbnail image. When such a display mode is adopted,the evaluations on images that are difficult to check for image qualityvisually, such as thumbnail images, can be favorably checked withoutmagnification or the like. In this case, the CPU of the personalcomputer functions as a controller which provides a pop-up display fordisplaying the evaluation results attached to the corresponding imageinformation based on designated position information for indicating theposition designated with the mouse cursor or the like.

Moreover, since the evaluation results are attached to the imageinformation, these evaluation results are accessible to the printingapparatus. The image information can thus be printed after correction.This makes it possible to improve the printing quality with no specialoperation.

Other Embodiments

The foregoing embodiment has dealt with the printer 1 as a printingapparatus. This embodiment, however, has been given in order tofacilitate the understanding of the present invention, not to limit theinterpretation of the present invention. It is understood thatmodifications and changes may be made without departing from the gist ofthe present invention, and all such equivalents are intended to fallwithin the scope of the present invention. In particular, embodiments tobe described below shall also be covered by the present invention.

First, in the foregoing embodiment, the storage medium for storing theimage information to be backed up (corresponding to the first storagemedium) is not limited to the memory card 110. For example, a hard diskdrive of the host computer 130 and a flexible disk loaded into this hostcomputer 130 are also applicable. Similarly, the storage medium forimage information to be backed up to (corresponding to the first storagemedium) is not limited to the CD-R 104. For example, a magneto-opticdisk may be used. In this case, the printer 1 is connected with anmagneto-optic disk drive (not shown). Moreover, the apparatus for makingan image evaluation is not limited to the printer 1. For example, it maybe a personal computer on which a retouch software application (imageprocessing software application) runs. The digital camera 120 is alsoapplicable.

As for the image evaluation procedure, the foregoing embodiment hasdealt with the case where evaluations are made on all the imageinformation to be backed up before the image information to be backed upis fixed. Then, backup is performed on the fixed image information. Inthis respect, evaluations, backup confirmation, and backup may beperformed as a set on each piece of image information. Moreover, theevaluation items are not limited to the ones described. Besides, theitems from which a comprehensive evaluation is made have only to beplural in number. That is, two may be applicable, and four or more aswell.

1. An image evaluation method comprising: (a) a step of acquiring, by acontroller, image information to be evaluated; (b) a step of setting, bythe controller, an evaluation area for the acquired image information,the evaluation area being smaller than an area corresponding to theimage information; (c) a step of obtaining, by the controller, anindividual evaluation on the acquired image information, the individualevaluation being obtained by individually evaluating a predetermineditem in the set evaluation area; (d) a step of obtaining, by thecontroller, an other individual evaluation on the acquired imageinformation, the other individual evaluation being obtained byindividually evaluating an other predetermined item; and (e) a step ofobtaining, by the controller, a comprehensive evaluation based on theindividual evaluation and the other individual evaluation, wherein: thepredetermined item is one selected from between an item indicating adegree of defocus and an item indicating a degree of a motion blur; andthe other predetermined item includes at least one item out of the itemindicating the degree of defocus, the item indicating the degree of amotion blur, and an item indicating a degree of noise occurring atcompression time, the at least one item being an item other than the oneselected as the predetermined item.
 2. An image evaluation methodaccording to claim 1, wherein in the step of setting the evaluationarea, the acquired image information is divided into a plurality ofblocks, and the evaluation area is set based on a block having a maximumnumber of edges.
 3. An image evaluation method according to claim 1,wherein in the step of setting the evaluation area, a luminance of eachpixel is acquired from the acquired image information, and theevaluation area is set so that a section in which a difference in theluminance of adjacent pixels is largest is located at the center.
 4. Animage evaluation method according to claim 1, wherein the otherpredetermined item includes two items out of the item indicating thedegree of defocus, the item indicating the degree of a motion blur, andthe item indicating the degree of noise occurring at compression time,the two items being items other than the one selected as thepredetermined item.
 5. An image evaluation method according to claim 1,wherein the other individual evaluation is obtained by individuallyevaluating the other predetermined item in the set evaluation area. 6.An image evaluation method comprising: (a) a step of acquiring, by acontroller, image information to be evaluated; (b) a step of setting, bythe controller, an evaluation area for the acquired image information,the evaluation area being smaller than an area corresponding to theimage information; (c) a step of obtaining, by the controller, anindividual evaluation on the acquired image information, the individualevaluation being obtained by individually evaluating a predetermineditem in the set evaluation area; (d) a step of obtaining, by thecontroller, an other individual evaluation on the acquired imageinformation, the other individual evaluation being obtained byindividually evaluating an other predetermined item; and (e) a step ofobtaining, by the controller, a comprehensive evaluation based on theindividual evaluation and the other individual evaluation, wherein: theother individual evaluation is obtained by individually evaluating theother predetermined item in the set evaluation area; and in the step ofsetting the evaluation area, the acquired image information is dividedinto a plurality of blocks, and the evaluation area is set based on ablock having a maximum number of edges.
 7. An image evaluation methodcomprising: (a) a step of acquiring, by a controller, image informationto be evaluated; (b) a step of setting, by the controller, an evaluationarea for the acquired image information, the evaluation area beingsmaller than an area corresponding to the image information; (c) a stepof obtaining, by the controller, an individual evaluation on theacquired image information, the individual evaluation being obtained byindividually evaluating a predetermined item in the set evaluation area;(d) a step of obtaining, by the controller, an other individualevaluation on the acquired image information, the other individualevaluation being obtained by individually evaluating an otherpredetermined item; and (e) a step of obtaining, by the controller, acomprehensive evaluation based on the individual evaluation and theother individual evaluation, wherein: the other individual evaluation isobtained by individually evaluating the other predetermined item in theset evaluation area; and in the step of setting the evaluation area, aluminance of each pixel is acquired from the acquired image information,and the evaluation area is set so that a section in which a differencein the luminance of adjacent pixels is largest is located at the center.8. An image evaluation method according to claim 1, wherein in the stepof obtaining the comprehensive evaluation, the comprehensive evaluationis obtained based on the individual evaluation and the other individualevaluation given weights, respectively.
 9. An image evaluation methodcomprising: (a) a step of acquiring, by a controller, image informationto be evaluated; (b) a step of setting, by the controller, an evaluationarea for the acquired image information, the evaluation area beingsmaller than an area corresponding to the image information; (c) a stepof obtaining, by the controller, an individual evaluation on theacquired image information, the individual evaluation being obtained byindividually evaluating a predetermined item in the set evaluation area;(d) a step of obtaining, by the controller, an other individualevaluation on the acquired image information, the other individualevaluation being obtained by individually evaluating an otherpredetermined item; and (e) a step of obtaining, by the controller, acomprehensive evaluation based on the individual evaluation and theother individual evaluation, wherein: in the step of obtaining thecomprehensive evaluation, the comprehensive evaluation is obtained basedon the individual evaluation and the other individual evaluation givenweights, respectively; and in the step of obtaining the comprehensiveevaluation, the comprehensive evaluation is obtained based on theindividual evaluation and the other individual evaluation given weights,respectively, the weights increasing with increasing impact on imagequality.
 10. An image evaluation method according to claim 9, wherein inthe step of obtaining the comprehensive evaluation, the comprehensiveevaluation is obtained by using a Gaussian function.
 11. An imageevaluation method according to claim 1, further comprising an evaluationattachment step of attaching the individual evaluation, the otherindividual evaluation, and the comprehensive evaluation to the acquiredimage information.
 12. An image evaluation method according to claim 1,further comprising a step of obtaining an individual evaluation not tobe included in the comprehensive evaluation by individually evaluatingthe acquired image information for a nontarget item that is not to beincluded in the comprehensive evaluation.
 13. An image evaluation methodcomprising: (a) a step of acquiring, by a controller, image informationto be evaluated; (b) a step of setting, by the controller, an evaluationarea for the acquired image information, the evaluation area beingsmaller than an area corresponding to the image information; (c) a stepof obtaining, by the controller, an individual evaluation on theacquired image information, the individual evaluation being obtained byindividually evaluating a predetermined item in the set evaluation area;(d) a step of obtaining, by the controller, an other individualevaluation on the acquired image information, the other individualevaluation being obtained by individually evaluating an otherpredetermined item; (e) a step of obtaining, by the controller, acomprehensive evaluation based on the individual evaluation and theother individual evaluation; and a step of obtaining, by the controller,an individual evaluation not to be included in the comprehensiveevaluation by individually evaluating the acquired image information fora nontarget item that is not to be included in the comprehensiveevaluation, wherein in the step of obtaining the individual evaluationnot to be included in the comprehensive evaluation, an item indicating adegree of focus on an area different from a subject intended forshooting is evaluated as the nontarget item.
 14. An image evaluationmethod comprising: (a) a step of acquiring, by a controller, imageinformation to be evaluated; (b) a step of setting, by the controller,an evaluation area for the acquired image information, the evaluationarea being smaller than an area corresponding to the image information;(c) a step of obtaining, by the controller, an individual evaluation onthe acquired image information, the individual evaluation being obtainedby individually evaluating a predetermined item in the set evaluationarea; (d) a step of obtaining, by the controller, an other individualevaluation on the acquired image information, the other individualevaluation being obtained by individually evaluating an otherpredetermined item; (e) a step of obtaining, by the controller, acomprehensive evaluation based on the individual evaluation and theother individual evaluation; a step of obtaining, by the controller, anindividual evaluation not to be included in the comprehensive evaluationby individually evaluating the acquired image information for anontarget item that is not to be included in the comprehensiveevaluation; and another evaluation attachment step of attaching, by thecontroller, the individual evaluation not to be included in thecomprehensive evaluation to the acquired image information.
 15. An imageevaluation device comprising: a memory for storing image information tobe evaluated; and a controller that performs: a step of acquiring theimage information to be evaluated; a step of setting an evaluation areafor the acquired image information; a step of obtaining an individualevaluation on the acquired image information, the individual evaluationbeing obtained by individually evaluating a predetermined item in theset evaluation area; a step of obtaining an other individual evaluationon the acquired image information, the other individual evaluation beingobtained by individually evaluating an other predetermined item; and astep of obtaining a comprehensive evaluation based on the individualevaluation and the other individual evaluation; wherein: thepredetermined item is one selected from between an item indicating adegree of defocus and an item indicating a degree of a motion blur; andthe other predetermined item includes at least one item out of the itemindicating the degree of defocus, the item indicating the degree of amotion blur, and an item indicating a degree of noise occurring atcompression time, the at least one item being an item other than the oneselected as the predetermined item.
 16. An image evaluation deviceaccording to claim 15, wherein: the memory stores image information tobe printed as the image information to be evaluated; and the controllerfurther performs a step of attaching the individual evaluation, theother individual evaluation, and the comprehensive evaluation to theimage information to be evaluated to generate image information intendedfor printing.
 17. A computer-readable medium having a program storedthereon, the program comprising: a code for making an image evaluationdevice perform a step of acquiring image information to be evaluated; acode for making the image evaluation device perform a step of setting anevaluation area for the acquired image information, the evaluation areabeing smaller than an area corresponding to the image information; acode for making the image evaluation device perform a step of obtainingan individual evaluation on the acquired image information, theindividual evaluation being obtained by individually evaluating apredetermined item in the set evaluation area; a code for making theimage evaluation device perform a step of obtaining an other individualevaluation on the acquired image information, the other individualevaluation being obtained by individually evaluating an otherpredetermined item; and a code for making the image evaluation deviceperform a step of obtaining a comprehensive evaluation based on theindividual evaluation and the other individual evaluation, wherein: thepredetermined item is one selected from between an item indicating adegree of defocus and an item indicating a degree of a motion blur; andthe other predetermined item includes at least one item out of the itemindicating the degree of defocus, the item indicating the degree of amotion blur, and an item indicating a degree of noise occurring atcompression time, the at least one item being an item other than the oneselected as the predetermined item.